Air Quality Model Evaluation International Initiative (AQMEII): Advancing the State of the Science in Regional Photochemical Modeling and Its Applications
Bibliographic record
Abstract
In early 1980's, American Meteorological Society (AMS) and U.S. Environmental Protection Agency (EPA) held two workshops to discuss and recommend methods for evaluating plume dispersion models (Fox, 1981 and 1984). AMS and EPA also held another workshop in 1984 to discuss evaluation issues relating to regional-scale air quality models, but the workshop participants did not recommend any specific methods for the model performance evaluation (Demerjian, 1985). Hence, the statistical metrics identified by the first AMS and EPA workshop continue to be used for evaluating Gaussian dispersion models as well as numerical regional-scale air quality models, not only in the United States but also in other countries. Although the focus in 1970¿s and 1980¿s was primarily on urban air pollution models, it is well-known that pollution problems such as acid rain, ozone, and fine particulate matter are regional in scope, requiring regional-scale multi-pollutant models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".